Grossberg teaches (Studies of Mind and Brain, Stephen Grossberg, Reidel Publishing Co., Boston, 1982) adaptive neural networks which can be trained to recognize an input distribution and also to recall previously memorized distributions. The former networks are called instars and the latter are called outstars. He further teaches self-regulating subnetworks which provide short-term memory, reset, and activity normalization, and are called shunting, recurrent, inhibitory on-center/off-surround networks.
Symbolic substitution consists of first identifying a given symbol or pattern, and then substituting in its place another symbol or pattern. If the patterns are devised to correspond to binary numbers, and if the replacement rule is devised to correspond to arithmetic rules, then symbolic substitution can be used to design digital computing architectures. This is how the basic relationship can be reduced to practice: Use an instar to recognize a pattern and an outstar to write another pattern and use neural network techniques to perform the substitution act.